Tana River County SMART Survey Report

February 2019

ACKNOWLEDGEMENT Tana River County SMART survey was made successful through the contribution of a number of partners. The County Department of Health led in the survey management.

The County appreciates the immense support accorded by partners at different levels in making this year’s SMART survey a success. These partners include; European Union, UNICEF, Concern Worldwide and Kenya Red Cross.

Special gratitude to; The Tana River County Nutrition Technical Forum and Nutrition Information Technical Working Group for their technical guidance during the survey, County government of Tana River for creating an enabling environment during the data collection exercise , Tana River County community for taking time to provide information which will be of importance in making informed decisions in the nutrition sector programming and last but not least the dedicated survey team members who worked hard in ensuring quality data is collected during this exercise.

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LIST OF ABBREVIATIONS ARI Acute Respiratory Infection

FAO Food and Agriculture Organization

BCG Bacillus Chalmette Guerin

CIDP County Integrated Development Plan

CLTS Community Led Total Sanitation

CSG County Steering Group

CHS Community Health Strategy

CSI Coping Strategy Index

ENA Emergency Nutrition Assessment

GAM Global Acute Malnutrition

IPC Integrated Phase Classification

KEPI Kenya Expanded Program on Immunization

MNPs Micronutrients Powders

MUAC Mid Upper Arm Circumference

NDMA National Drought Management Authority

OPV Oral Polio Vaccine

PLW Pregnant and lactating women

SAM Severe Acute Malnutrition

SBCC Social Behavior Change and Communication

SMART Standardized Monitoring Assessment on Relief and Transition

SPSS Statistical Package for Social Sciences

UNICEF United Nation Children Fund

WASH Water hygiene and Sanitation

WHO World Health Organization.

CNC County Nutrition Coordinator

NSO Nutrition Support Officer

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IPs Implementing Partners

CONTENTS

Contents ACKNOWLEDGEMENT ...... 2 LIST OF ABBREVIATIONS ...... 3 LIST OF TABLES ...... 7 LIST OF FIGURES ...... 8 EXECUTIVE SUMMARY ...... 9 1.0. INTRODUCTION ...... 14 1.1. Background ...... 14 1.2. Survey Justification ...... 14 1.4. Survey Timing ...... 15 2.0. METHODOLOGY ...... 16 2.1. Survey Design...... 16 2.2. Sampling Procedure ...... 16 2.2.1. Study Population ...... 16 2.2.2. Sampling methods and sample size calculation ...... 16 2.2.3. Sample Size Description ...... 17 2.3. Data Collection...... 17 2.4. Data Collection Tools and Variables ...... 17 2.5. Data Analysis ...... 18 2.6. Data Quality Control Measures ...... 18 3.0. RESULTS ...... 19 3.1. General Characteristics of Study Population ...... 19 3.2. Distribution of Age and Sex (Under-fives) ...... 20 3.3. Under-fives Nutrition Status ...... 20 3.3.1. Prevalence of Acute Malnutrition (Wasting) ...... 20 3.4. Children Morbidity and Health Seeking ...... 25 3.4.1. Therapeutic Zinc Supplementation during watery diarrhea episodes ...... 26 3.4.2. Health Seeking ...... 27

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3.5. Child Immunization, Vitamin A and Micronutrients Supplementation and Deworming 27 3.5.1. Immunization ...... 27 3.5.2. Vitamin A Supplementation and Deworming ...... 28 3.6. Maternal Nutrition...... 29 3.7. Water Sanitation and Hygiene Practices ...... 30 3.7.1 Main Water Sources, Distance and Time to Water Sources ...... 30 3.7.2. Water Treatment ...... 31 3.7.3. Water Storage and Payment ...... 32 3.7.4. Hand washing...... 32 3.7.5. Sanitation Facilities Ownership and Accessibility ...... 33 3.8. Household and Women Dietary Diversity ...... 34 3.8.1. Household Dietary Diversity (HDD) ...... 34 3.9. Food Consumption Score (FCS) ...... 35 3.10. Coping Strategy Index (CSI) ...... 36 4.0. CONCLUSION AND RECOMMENDATIONS ...... 39 4.1. Conclusion...... 39 4.2. Recommendations ...... 40 REFERENCES ...... 41 APPENDICIES ...... 41 APPENDIX 1: Overall Score of the Survey ...... 41 Appendix 2: Sampled Clusters ...... 42 Appendix 3: Calender of Events ...... 45 Appendix 4: SMART Questionnaire ...... 48 Appendix 5: Survey teams ...... 72 Appendix 6: Survey coodination ...... 73

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LIST OF TABLES Table 1: Results Summary ...... 10 Table 2:Tana River Seasonal Calendar ...... 15 Table 3: Sample size calculation ...... 16 Table 4: Main source of income for household head ...... 19 Table 5:Main occupation of household head ...... 19 Table 6 Age and sex distribution of children 6 to 59 months...... 20 Table 7 Prevalence of acute malnutrition based on Weight for Height Z- score...... 21 Table 8: Prevalence of acute malnutrition by age based on WFH Z- score and or oedema ...... 22 Table 9: Prevalence of acute malnutrition and Edema based on WFH Z score ...... 22 Table 10: Prevalence of Acute malnutrition based on MUAC Cut offs (and or Oedema) and by sex ...... 23 Table 11: Prevalence of underweight based on WFA Z score and by sex ...... 24 Table 12: Prevalence of stunting based on Height for Age Z-score and by sex ...... 24 Table 13: Stunting by age ...... 25 Table 14: Children Morbidity ...... 26 Table 15: Maternal Nutrition Status ...... 30 Table 16: IFAS Consumption in days ...... 30 Table 17: Distances to water sources ...... 31 Table 18: Hand washing practices ...... 32 Table 19: Household relieving point ...... 33 Table 20: Minimum Women Dietary Diversity ...... 35 Table 21: Food consumption score ...... 36 Table 22: Coping Strategies ...... 37

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LIST OF FIGURES

Figure 1:Tana River map showing livelihood zones ...... 14 Figure 2: Graphical representation of WFH for children assessed compared to reference children ...... 21 Figure 3: Trends of wasting prevalence in Tana River County ...... 23 Figure 4: Graphical representation of HFA distribution in comparison with WHO standards ...... 25 Figure 5: Health Seeking Places ...... 27 Figure 6: Immunization Coverage...... 28 Figure 7: Trends of Vitamin A supplementation...... 29 Figure 8: : Physiological status of WRA ...... 29 Figure 9: Main sources of drinking water ...... 31 Figure 10: Treatment of drinking water ...... 32 Figure 11: Relieving points ...... 33 Figure 12: Foods consumed at the household level based on 24 hours recall ...... 34 Figure 13: Women Dietary Diversity based on 24 hour recall ...... 35 Figure 14:Micronutrient analysis based on FCS ...... 36 Figure 15: CSI Trend ...... 38

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EXECUTIVE SUMMARY Introduction

Tana River County is located in the Kenyan Coastal region and is divided in to 3 sub counties namely; Bura, Galole and Garsen. The County has three main livelihood zones namely; Pastoral, Marginal mixed farming and Mixed farming. The County department of health with support from UNICEF and Implementing Partners carried out a SMART survey in the entire County in February 2019. The main objective of the survey was to determine the prevalence of malnutrition among the children aged 6 - 59 months, pregnant and lactating mothers in Tana River County. Specifically, the survey aimed at determining the nutrition status of children 6 to 59 months, the nutritional status of women of reproductive age (15-49 years) based on maternal mid upper arm circumference, immunization coverage; measles (9-59 months), OPV1/3 and Vitamin A for children aged 6- 59months. The survey also was meant to determine deworming coverage for children aged 12 to 59 months, the prevalence of common illnesses as well to assess maternal and child health care practices, water, sanitation and hygiene practices and prevailing food security situation in the County.

Methodology

The survey was cross sectional and descriptive by design. Standardized Monitoring and Assessment on Relief and Transition methodology was be adopted in the study. The study applied quantitative approach. Two stage sampling was used in the survey. The first stage involved random selection of clusters from the sampling frame based on probability proportion to population size (PPS)1.Emergency Nutrition Assessment (ENA) for Standardized Monitoring for Assessment for Relief and Transition (SMART) July 2015 was used in calculation of sample size. Household was used as the sampling unit in the second stage sampling or basic Sampling Unit. The sample size obtained using ENA software (658 households) was used as the survey sample size. Based on logistical factors (time taken to arrive from the clusters, introductions, sampling, inter household movement, lunch and time back to the base), it was possible to visit 16 households per cluster per day translating to a minimum of 42 clusters. Simple random sampling was used in household selection. Led by a village guide, the survey teams developed a sampling frame in each of the village sampled during the first stage sampling in case such a list never existed.

For the data collection purpose, electronic questionnaire was used. Anthropometric data processing was done using ENA software version 2015 (July). All the other quantitative data were analyzed in Ms. Excel and the SPSS (Version 20) computer package.

1 In this method villages with more population are likely to be selected as compared to those with low population

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Table 1: Results Summary

RESULTS SUMMARY

ANTHROPOMETRIC RESULTS % with 95% WHO 2006 Standards N N % with 95% CI CI Design effect (WHZ)= 1.13 Feb-18 Feb-19 14.8 % 15.6 % Prevalence of GAM based on (11.7 - 18.4 95% 628 (11.6 - 20.6 644 WHZ (-2 z score C.I.) 95% )

2.6 % Prevalence of SAM based on 2.2 % (1.7 - 4.2 95% WHZ (-3 z score) and/or 628 (1.2 - 4.0 95 644 C.I.) edema %.)

22.6 % 21.7 % Prevalence of stunting based 614 (19.3 - 26.4 630 (18.2 - 25.8 95% on HFA (<-2 z-score) 95% ) C.I.) 6.3 % Prevalence of severe 5.4 % (4.7 - 8.5 95% stunting based on HFA(<-3 z 614 (3.8 - 7.5 95% 630 C.I.) score) )

23.3 % 23.5 % Prevalence of underweight (19.8 - 27.2 95% 631 (18.6 - 29.1 653 based on WFA(<-2 z score) C.I.) 95% )

Prevalence of severe 5.4 % 4.6 % underweight based on 631 (3.5 - 8.2 95% 653 (3.1 - 6.7 95% WFA(<-3 z score) ) C.I.) CHILD MORBIDITY (Based on 2 Weeks Recall Feb 2019 Indicator Type of Illness % Feb 2018 % Feb 2019 (n) All 51.3% 233 35.2% Fever with 68% 105 37.9% Chills Illness in the last 2 weeks ARI 8% 115 41.5% (Children 6 to 59 months) Watery 19% 35 12.6% diarrhea Bloody 1% 0 0% diarrhea

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Therapeutic Zinc supplementation during 57% N (35) 100% diarrhea episodes VITAMIN A SUPPLEMENTATION AND DEWORMING Feb 2019 Indicator No. of Times % Feb 2018 % Feb 2019 (n) Vitamin A supplementation At least Once 75% 58 66.7% 6 to 11 Months Vitamin A supplementation 207 58.1% 6 to 59 Months(once) Vitamin A supplementation At least Once 77% 314 56.1% 12 to 59 months (Once) Vitamin A Supplementation At least twice 41.7% 146 48.0% 12 to 59 months Deworming (12 to 59 At least Once 40.4% 193 33.6% Months) At least Twice 12.4% 59 10.3% IMMUNISATION Means of Feb 2019 Antigen % Feb 2018 % Feb 2019 Verification (n) Presence of a BCG 95.9% 625 94.4% Scar OPV1 Recall and Card 96.7% 637 96.6% OPV 3 Recall and Card 92.3% 626 95.0% Measles at 9 months Recall and Card 87.9% 568 91.8% Measles at 18 Months Recall and Card 71.2% 357 71.8% MATERNAL NUTRITION Feb 2019 Indicator Description % Feb 2018 % Feb 2019 (n) Women of MUAC < 21.0 cm Reproductive 6.1% 18 3.1% age Pregnant and MUAC< 21.0 cm lactating 4.4% 6 1.7% women Mothers with Women supplemented with children less 86.3% 298 50.9% FeFo than 2 years At least 270 Women Consuming FeFo 0.5% 1 0.4% days At least 90 days 73.6% 164 64.8%

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Mean Number Average IFAS Consumption of days FeFo 57.6 days was consumed WATER SANITATION AND HYGIENE PRACTISES Feb 2019 Indicator Description % Feb 2018 % Feb 2019 (n) Households obtaining water All Households 72.9% 505 75.8 from safe sources Households obtaining water All Households 69.6% 486 73.0% from sources less than 500m Households treating their All Households 23.6% 113 17% water Households Hand washing in 4 critical with children 9.5% 58 12.6% moments (N= 247) under 2 years Proportion of households All Households 43.1% 283 42.9% that owns a toilet Proportion of households All Households 56.9% 380 57.1% practicing open defecation HOUSEHOLD AND WOMEN DIETARY DIVERSITY FOOD CONSUMPTION SCORE AND COPING STRATEGY INDEX Indicator % Feb 2018 Feb 2019(n) % Feb 2019 Household within Acceptable food 66.1% 577 86.6% consumption score (>35.5) Coping Strategy Index 18.1% 18.1%

Conclusion

Overall the nutrition Status of children in Tanariver County remained the same ccompared to the outcome of a SMART survey conducted in the same season in 2018. There was no significant statistical difference between wasting for children under-five years between the SMART Nutrition survey conducted in February 2018 (GAM 15.6%) and February 2019 (GAM 14.8%) p=0.7723 There was also no significant statistical difference between other childhood malnutrition indicators; underweight and stunting. The county is classified to be in phase 2 (Serious) according to IPC classification for acute malnutrition.

Analysis was done on food security and morbidity issues, which would have contributed to changes in acute malnutrition. According to the February 2019 SRA report, Tanariver county food security phase classification is Stressed (IPC phase 2). Pastoral and Marginal mixed farming zones classified to be in crisis phase (IPC phase 3) and on a worsening trend. Mixed farming zone classified to be in stressed phase and the trend worsening. Tana North sub county is currently the worst affected closely followed by Galole and Tana Delta sub counties respectively. Although there was no significant difference between 2018 and 2019 surveys, the stunting and underweight

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levels remained relatively high that requires County interventions. There was no significant difference in the two indicators between boys and girls.

Maternal nutrition status based on MUAC measurement among all women of reproductive age and pregnant and lactating women only showed an improvement with the two categories having MUAC of <21cm at 3.1% and 1.7% respectively in 2019 an improvement from 6.1% and 4.4% in 2018 respectively

Average IFAS consumption mean number of days FeFo was consumed was found to be 57.6 days out of the minimum required days of 180. More advocacy on FeFo utilization is needed to help increase the uptake.

In conclusion it was noted that key drivers of poor nutritional status in Tanariver County include; Chronic food insecurity, Inadequate dietary diversity, Poor access to safe water, Diseases, Poor hygiene and sanitation practices.

Recommendation

⮚ Active case findings at the community and Nutrition surveillance. ⮚ Scale up of IMAM surge activities at health facilities implementing IMAM. ⮚ Continued scale up of MIYCN activities (BFCI and BFHI) as well as IMAM activities ⮚ Conduct rapid assessment in areas identified as malnutrition hotspots ⮚ Upscale integrated medical outreaches and mobile clinics in the county especially within the two most affected sub counties (Tana North and Tana river) ⮚ Introduce cash transfer program for the affected sub counties for at least three months - Establish a Multi-sectoral platform for high level advocacy and coordination of nutrition activities both sensitive and specific and advocate for recruitment of more nutritionist to help boost nutrition service delivery. Sensitize the community on the importance of Vitamin A supplementation and deworming as well as scale up VAS interventions within ECDE, Duks and at the community. ⮚ Sensitize HCP on proper/appropriate documentation ⮚ Strengthen health education to community on important of IFAS uptake and early ANC visit (SBCC) ⮚ Train HCP on IFAS policy guidelines as well as MNPS ⮚ Adopt and fully operationalize CLTS in the county ⮚ Operationalize the community health units to ensure strengthened referral from the community ⮚ Community health education on importance of treated drinking water ⮚ Procurement and distribution of water treatment chemical; as the preferred method of treatment.

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1.0. INTRODUCTION

1.1. Background Tana River County is located in the Coastal region of Kenya, which occupies an area of approximately 38,437 km2, has an estimated population of 324,054 people. Tana River County borders Kitui County to the West, Garissa County to the North East, Isiolo County to the North, Lamu County to the South East and Kilifi County to the South. The County has three sub counties namely: Bura, Galore and Garsen. Tana River County has four main livelihood zones namely; Pastoral, Marginal mixed farming, Mixed farming and National park as shown in figure 1.

Generally, the county experiences bimodal rainfall pattern which is mostly erratic with long rains falling between April and June and short rains between October and December.

The pastoral and marginal mixed farming livelihood zones rely on short rains while mixed farming zone rely on long rains. The mean annual rainfall ranges between 220mm and 500mm except the mixed farming zone, which receives rainfall ranging between 750mm and 1250mm. The County is generally hot and dry with temperatures ranging between 21°C and 38°C with the coldest month in July and hottest months in September and January. It therefore experiences two dry spells every year occurring in December to March and July to October.

Figure 1:Tana River map showing livelihood zones

Most of the County consists of low-lying plains with the highest points being Minjila and Bilbil. The River Tana traverses the County from Tharaka Nithi County in the North to the Indian Ocean in the South passing through Tana Delta and covering a stretch of approximately 500km, situated in the Eastern side of the county, this provides livelihood opportunity to resident population through flood receded crop farming.

1.2. Survey Justification According to a SMART survey carried out in Tanariver County (January/February 2018), the Global Acute Malnutrition was at critical Phase (15.6 %) while SAM was at 2.2% and Stunting was at 22.6%. The December 2018 NDMA bulletin put the County at Alert phase of drought cycle,

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experiencing vegetation deficit within all the 3 sub counties and milk production and consumption at the Household level was poor and below normal. The purpose of the survey was to assess the nutrition situation of children below five years and women of reproductive age in Tana River County. The survey results will feed into the Short Rains Assessments report, which will inform the response plan.

1.2. Survey Objectives

The main objective of the survey was to determine the prevalence of malnutrition among children aged 6- 59 months old, pregnant and lactating mothers in Tana River County.

1.3. Specific Objectives

▪ To assess current prevalence of acute malnutrition in children aged 6-59 months. ▪ To determine the nutritional status of women of reproductive age (15-49 years) ▪ To determine immunization coverage for measles, OPV1 & 3 and Vitamin A for children aged 6-59 months. ▪ To determine deworming coverage for children aged 12 - 59 months. ▪ To determine the prevalence of common illnesses (diarrhea, measles and ARI). ▪ To assess water, sanitation and hygiene practices. ▪ To establish the coverage of iron/folic acid supplementation and consumption during pregnancy among lactating women ▪ To assess health seeking behavior among caregivers of children below 5years ▪ To assess the prevailing situation of household food security in the County.

1.4. Survey Timing Tana River SMART survey was done in February 2019. According to the County seasonal calendar, this is usually a short dry spell. At this season, communities in the mixed farming livelihood zone have their farm without crop. Pastures depleting to be considered dry season in the pastoral communities. Table 2 below is the seasonal calendar for Tana River County

Tana River SMART Survey Table 2: Tana River Seasonal Calendar 2019 Short dry spell Long rains Long dry spell Short rains

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Short rains harvest Land Plantin Crops at Long Land Plantin Crops at Prepara g/weedi green rains Prepara g/Weed green tion ng maturit harvest tion. ing Lean y Lean Maturit period period y for for farmers farmers Source: National Drought Management Authority

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2.0. METHODOLOGY

2.1. Survey Design The survey was cross sectional and descriptive by design. The study adopted Standardized Monitoring & Assessment on Relief and Transition’s methodology. The study applied quantitative approach.

2.2. Sampling Procedure

2.2.1. Study Population The study population included the entire population in Tana River County, estimated at 324,054 people. All villages (clusters/sampling units) in the County, which were accessible, secure and not deserted, were included in the sampling frame.

2.2.2. Sampling methods and sample size calculation Anthropometric sample size calculation

The survey adopted Two-stage sampling technique. The first stage involved random selection of clusters from the sampling frame based on probability proportion to population size (PPS)2.Emergency Nutrition Assessment (ENA) for Standardized Monitoring for Assessment for Relief and Transition (SMART) July 2015 was used in calculation of sample size.

Table 3: Sample size calculation

Parameters for Value Assumptions based on context Anthropometry Estimated Prevalence of 15.6 % Based on January 2018 prevalence. The County drought GAM (%) status is Alert and the trend is worsening in all the livelihood zones (EWS bulletin December 2018). This status is expected to worsen. ± Desired precision 3.% Rule of Thumb Design Effect 1.13 Based on January 2018 SMART Survey Children to be included 691 Average HH Size 6 Based on CIDP % Children under-5 20.06% Based on 2009 population census projections %Non-response 3 % Estimated non response based on the current situation Households population migration Households to be included 658 Number of households per 16 day Based on 2018 SMART Survey Experience Number of clusters 42 Computed from the Number of HHs per Day

2 In this method villages with more population are likely to be selected as compared to those with low population

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2.2.3. Sample Size Description Household was used as the sampling unit in the second stage sampling or basic Sampling Unit. The sample size obtained using ENA software (658 households) was used as the survey sample size. Based on logistical factors (time taken to arrive from the clusters, introductions, sampling, inter household movement, lunch and time back to the base), it was possible to visit 16 households per cluster per day translating to a minimum of 42 clusters. Simple random sampling was used in household selection. Led by a village guide, the survey teams developed a sampling frame in each of the village sampled during the first stage sampling in case such a list never existed. From the list, the survey teams randomly selected 16 households where they administered household questionnaire (in all households) and anthropometric, morbidity and immunization questionnaire in household with children aged 6 to 59 months.

2.3. Data Collection Data Collection was done for 6 days (08th to 13th of February 2019) by seven teams. Every team was composed of four members who included a team leader, 2 measurers and a community guide. Teams were trained for 4 days prior to field work. Teams were trained on, the survey objectives, methodology, malnutrition diagnosis, anthropometric measurements, sampling methods, data collection tools, ODK data collection process as well as interviewing skills. A role play was included in the training to give the teams practical skills on data collection. On the 3rd day standardization test was done. To evaluate team’s accuracy and precision in taking anthropometric measurements. SMART data collection tool was piloted / tested in a non-selected cluster to be part of the survey sample. Additionally, during the piloting the enumerators were required to undertake the entire process of the survey, which included household selection, taking anthropometric measurements and filling of the data collection forms.

The overall survey coordination was handled by the Tana River County Nutrition Coordinator with support from the Nutrition Support Officer, 3 sub county nutritionist and Implementing Partners on training and supervision of survey teams, as well as technical guidance from NITWG. Close supervision was conducted to ensure data collected during the survey is of high quality. The supervisor’s main responsibilities were to ensure that the methodology was followed, measurements were taken appropriately and tackling any technical issue which came up during data collection. On daily basis plausibility were done and gaps noted were communicated to all the teams before going to the field every morning for corrections and adjustments.

2.4. Data Collection Tools and Variables For the data collection purpose, electronic questionnaire was used. Each questionnaire consisted of identification information, household information, demographic information, anthropometric information, morbidity, immunization, maternal, WASH and food security data. Household, demographic and food security information were collected in all the sampled households. The rest of the data was collected from only households with children aged 6 to 59 months.

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2.5. Data Analysis Anthropometric data processing was done using ENA software version 2015 (July). World Health Organization Growth Standards (WHO-GS) data cleaning and flagging procedures was used to identify outliers which would enable data cleaning as well as exclusion of discordant measurements from anthropometric analysis. The ENA software generated weight-for-height, height-for-age and weight-for-age z scores to classify them into various nutritional status categories using WHO standards and cut-off points and exported to SPSS for further analysis. All the other quantitative data were analyzed in Ms. Excel and the SPSS (Version 22) computer package.

2.6. Data Quality Control Measures To ensure data collected was valid and reliable for decision-making, a number of measures were put in place. They included;

I. Thorough 4 days training conducted to survey participants, the training dealt on SMART methodology, survey objectives, interviewing techniques and data collection tools.

II. Ensuring all anthropometric equipment were functional and standardized. On daily basis, each team was required to calibrate the tools.

III. During the training exercise, standardization test was done; in addition, piloting of tools was done to ensure all the information was collected with uniformity.

IV. Conducting a review of data collection tools during training and after the pilot test.

V. All the survey teams were assigned a supervisor during data collection.

VI. The anthropometric data collected was entered daily on ENA software and plausibility check was run. Any issues noted were communicated to the teams before they proceeded to the field the following day.

VII. Teams were supervised to ensure all errors were rectified on time. More attention was given to the teams with notable weaknesses.

VIII. Adequate logistical planning beforehand and ensuring the assigned households per clusters were be comfortably survey.

IX. A whatsup group was formed to ensure close monitoring of the teams and easier for communication.

X. Close supervision by the CNC, NSO and IPs during data collection period.

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3.0. RESULTS

3.1. General Characteristics of Study Population The survey involved collection of information from 662 children in 666 households. Only 7 sampled households did not participate in the survey. The response rate was therefore 99.0%. The reason for non-response were absenteeism and refusal to participate. The average household size recorded from this survey was 3.5. All households that participated in the survey were residents.

The main income sources of household heads were Casual labour (26.3%), sale of livestock products (19.4%), Sale of crops (14.9%) and other sources of income are as shown in table 4 below. In terms of occupation, majority of household heads were livestock herders (27.3%), waged laborers (23.6%) as well as own farm laborers as shown in table 5 below. While on school enrolments, 66% of children aged 3 to 18 years are enrolled in school, 34% were not. The reasons for non-enrollment included; parents felt their children to be young for school enrollment, no school was nearby, family labor responsibilities as well as the household could not see the need for the child being in school.

Table 4: Main source of income for household head

Main Source of Income Number Percentage No Income 44 6.6% Sale of Livestock 129 19.4% Sale of Livestock Products 40 6.0% Sale of crops 99 14.9% Petty trading e.g. sale of firewood 78 11.7% Casual Labor 175 26.3% Permanent Job 28 4.2% Sale of personal assets 2 0.3% Remittance 8 1.2% Others (Specify) 63 9.5%

Table 5:Main occupation of household head

Main Occupation of household head Numbers Percentage Livestock herding 182 27.3% Waged labor (Casual) 157 23.6% Own farm labor 146 21.9% Petty trade 62 9.3% Firewood/charcoal 21 3.2% Employed (salaried) 34 5.1% Fishing 6 0.9% Merchant/trader 5 0.8% Others (Specify) 53 8.0%

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3.2. Distribution of Age and Sex (Under-fives) The total number of children assessed during the survey was 644 (348 boys and 312 girls). The boy to girl ratio was 1.12 and a p-value of 0.161 (boys and girls equally represented) who participated in the survey. Table 6 below is a summary of sex and age distribution of children who were assessed. Age ratio of 6-29 months to 30-59 months: 0.98 (The value should be around 0.85), p-value = 0.075 which is as expected.

Table 6 Age and sex distribution of children 6 to 59 months

Boys Girls Total Ratio AGE (mo) no. % no. % no. % Boy:girl 6-17 93 55.4 75 44.6 168 25.5 1.2 18-29 89 56.3 69 43.7 158 23.9 1.3 30-41 85 54.1 72 45.9 157 23.8 1.2 42-53 63 47.0 71 53.0 134 20.3 0.9 54-59 18 41.9 25 58.1 43 6.5 0.7 Total 348 52.7 312 47.3 660 100.0 1.1

3.3. Under-fives Nutrition Status Under five nutrition status was assessed using anthropometric indicators namely, Weight for Height and MUAC (wasting or acute malnutrition), Height for Age (stunting or chronic malnutrition) and weight for age (underweight). Analysis was based on 2006 WHO reference standards.

3.3.1. Prevalence of Acute Malnutrition (Wasting) According to UNICEF nutrition glossary (2012), malnutrition is defined a state in which the body does not have enough of the required nutrients (under nutrition) or has excess of the required nutrients (over nutrition). Acute malnutrition is the low weight for height in reference to a standard child of a given age based on WHO growth standards. This form of malnutrition reflects the current form of malnutrition. Acute malnutrition can further be categorized as severe acute malnutrition and moderate acute malnutrition. Severe acute malnutrition is defined as weight for height < -3 standard deviation in comparison to a reference child of the same age. It also includes those children with bilateral edema as well as those with MUAC less than 11.5cm. Moderate Acute Malnutrition on the other hand is defined as weight for height >= -3 and <-2 standard deviation in comparison to a reference child of the same age and sex, but also include those children with MUAC < 12.5 cm and >= 11.5 cm. The global acute malnutrition (GAM) is the Sum of all children with moderate and severe acute malnutrition in the sample.

Prevalence of Acute Malnutrition based on Weight for Height by Sex

Analysis of acute malnutrition was based on 644 children aged 6 to 59 months (341 boys and 303 girls). There was an exclusion of 16 children who were flagged off as outliers. From the analysis,

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Tana River county global acute malnutrition was 14.8% (11.7- 18.4, 95% C.I.) The SAM rate in the County was 2.6 % (1.7-4.2, 95% C.I.).

Table 7 Prevalence of acute malnutrition based on Weight for Height Z- score

Indicator Total (N) All (% with Boys (% with 95% Girls (% with 95% 95% C.I) C.I) C.I)

Prevalence of global 644 (95) 14.8 % (46) 13.5 % (49) 16.2 % malnutrition (11.7 - 18.4 (10.1 - 17.8 95% (12.4 - 20.8 95% (<-2 z-score and/or oedema) 95% C.I.) C.I.) C.I.)

Prevalence of moderate 644 (78) 12.1 % (37) 10.9 % (41) 13.5 % malnutrition (9.2 - 15.7 (7.7 - 15.1 95% (9.9 - 18.2 95% (<-2 z-score and >=-3 z-score, 95% C.I.) C.I.) C.I.) no oedema)

Prevalence of severe 644 (17) 2.6 % (9) 2.6 % (8) 2.6 % malnutrition (1.7 - 4.2 (1.5 - 4.7 95% C.I.) (1.4 - 4.9 95% C.I.) (<-3 z-score and/or oedema) 95% C.I.)

The prevalence of Oedema was 0.0%

Figure 2 below is a graphical representation of distribution of weight for height of children surveyed in relation to the WHO standard curve (reference children). The curve slightly shifts to the left with a mean of -0.90 SD (±1.04) an indication of under nutrition in comparison to reference children.

Figure 2: Graphical representation of WFH for children assessed compared to reference children

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Analysis of Acute Malnutrition by Age

Further analysis was done on prevalence of acute malnutrition based on sex and age as indicated in table 8 below. From the analysis older children (30 to 59 months) were more affected by severe and moderate malnutrition as compared to younger children (6 to 29 months).

Table 8: Prevalence of acute malnutrition by age based on WFH Z- score and or oedema

Severe wasting Moderate Normal Oedema (<-3 z-score) wasting (> = -2 z score) (>= -3 and <-2 z-score )

Age (mo) Total no. No. % No. % No. % No. % 6-17 165 3 1.8 10 6.1 152 92.1 0 0.0 18-29 157 5 3.2 14 8.9 138 87.9 0 0.0 30-41 150 3 2.0 24 16.0 123 82.0 0 0.0 42-53 130 5 3.8 22 16.9 103 79.2 0 0.0 54-59 42 1 2.4 8 19.0 33 78.6 0 0.0 Total 644 17 2.6 78 12.1 549 85.2 0 0.0

Analysis of Acute Malnutrition based on presence of Oedema

Presence of bilateral edema is a sign of severe acute malnutrition. Analysis was therefore done based on this indicator. As shown in table 9 below, no edema case was recorded among the children surveyed.

Table 9: Prevalence of acute malnutrition and Edema based on WFH Z score

<-3 z-score >=-3 z-score

Oedema present Marasmic kwashiorkor Kwashiorkor No. 0 No. 0 (0.0 %) (0.0 %) Oedema absent Marasmic Not severely malnourished No. 26 No. 634 (3.9 %) (96.1 %)

Trends of Acute Malnutrition in Tana River County

There was no significant increase in Malnutrition based on WFH Z score compared to February 2018 survey (p=0.7723). The food security situation in Tana River County was classified as “Stressed” (IPC Phase 2) in the mixed farming and marginal farming zones whereas the pastoral livelihood is classified as “Crisis” (IPC Phase 3).

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Figure 3: Trends of wasting prevalence in Tana River County

Prevalence of Acute Malnutrition based on MUAC Malnutrition can also be diagnosed using MUAC. MUAC is a good indicator of muscle mass and can be used as a proxy of wasting (United Nation System Standing Committee on Nutrition). It is also a very good predictor of the risk of death. Very low MUAC (< 11.5 cm for children 6 to 59 months), is considered a high mortality risk and is a criteria for admission of outpatient therapeutic or in patient therapeutic program (when accompanied with complications) for treatment of severe acute malnutrition. A MUAC reading of 11.5 cm to <12.5 cm is considered as moderate malnutrition. Analysis of the nutrition status for children aged 6 to 59 months based on MUAC and or presence of Oedema resulted to GAM of 2.7% and SAM of 0.3% as indicated in table 10 below. Table 10: Prevalence of Acute malnutrition based on MUAC Cut offs (and or Oedema) and by sex

All Boys Girls n = 660 n = 348 n = 312 Prevalence of global malnutrition (18) 2.7 % (5) 1.4 % (13) 4.2 % (< 125 mm and/or oedema) (1.6 - 4.6 95% (0.5 - 3.8 95% (2.3 - 7.4 95% C.I.) C.I.) C.I.) Prevalence of moderate malnutrition (< (16) 2.4 % (4) 1.1 % (12) 3.8 % 125 mm and >= 115 mm, no oedema) (1.4 - 4.2 95% (0.3 - 3.7 95% (2.2 - 6.8 95% C.I.) C.I.) C.I.)

Prevalence of severe malnutrition (2) 0.3 % (1) 0.3 % (1) 0.3 % (< 115 mm and/or oedema) (0.1 - 1.2 95% (0.0 - 2.1 95% (0.0 - 2.3 95% C.I.) C.I.) C.I.)

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Prevalence of Underweight based on WFA Underweight is defined as low weight for age relative to National Centre for Health and Statistics or World Health Organization reference median. In this survey, the later was used. Children with weight for age less than -2 SD in relation to a reference child are classified as underweight while those with less than -3 SD are classified as severe underweight. Underweight is a composite form of under nutrition and has elements of both acute under nutrition (wasting) as well as chronic under nutrition (stunting). As indicated in table 11 below, the prevalence of underweight among children aged 6 to 59 months in Tana River County was 23.3% while those who were severely underweight was 4.6%.

Table 11: Prevalence of underweight based on WFA Z score and by sex

All Boys Girls n = 653 n = 344 n = 309 Prevalence of underweight (152) 23.3 % (77) 22.4 % (75) 24.3 % (<-2 z-score) (19.7 - 27.2 95% (17.5 - 28.1 95% (20.5 - 28.5 C.I.) C.I.) 95% C.I.) Prevalence of moderate underweight (122) 18.7 % (63) 18.3 % (59) 19.1 % (<-2 z-score and >=-3 z-score) (15.2 - 22.7 95% (13.8 - 23.9 95% (15.6 - 23.1 C.I.) C.I.) 95% C.I.) Prevalence of severe underweight (30) 4.6 % (14) 4.1 % (16) 5.2 % (<-3 z-score) (3.1 - 6.7 95% (2.2 - 7.4 95% C.I.) (3.2 - 8.3 95% C.I.) C.I.)

Prevalence of Chronic Malnutrition (Stunting) based on Height for Age (HFA).

WHO define stunting as height for age less than – 2 SD from median height for age of reference population. Childhood stunting is an outcome of maternal under nutrition as well as inadequate infant and young child feeding. It is associated with impaired neurocognitive development, a risk maker of non-communicable diseases and reduced productivity later in life (WHO 2013). Analysis of stunting prevalence based on height for age revealed an overall stunting rate of 21.7% In addition, a severe stunting (HFA< -3 in reference to standard population) rate of 6.3% as shown in table 12 below. Boys were more stunted than girls were. Table 13 illustrates stunting by age.

Table 12: Prevalence of stunting based on Height for Age Z-score and by sex

All Boys Girls n = 630 n = 335 n = 295 Prevalence of stunting (137) 21.7 % (82) 24.5 % (55) 18.6 % (<-2 z-score) (18.3 - 25.7 (19.3 - 30.5 95% (14.5 - 23.7 95% 95% C.I.) C.I.) C.I.) Prevalence of moderate stunting (97) 15.4 % (59) 17.6 % (38) 12.9 % (<-2 z-score and >=-3 z-score) (12.7 - 18.6 (13.7 - 22.3 95% (9.2 - 17.7 95% C.I.) 95% C.I.) C.I.) Prevalence of severe stunting (40) 6.3 % (23) 6.9 % (17) 5.8 %

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(<-3 z-score) (4.7 - 8.5 95% (4.4 - 10.6 95% (3.7 - 8.9 95% C.I.) C.I.) C.I.)

Table 13: Stunting by age

Severe stunting Moderate stunting Normal (<-3 z-score) (>= -3 and <-2 z- (> = -2 z score) score ) Age (mo) Total no. No. % No. % No. % 6-17 158 9 5.7 19 12.0 130 82.3 18-29 153 15 9.8 27 17.6 111 72.5 30-41 150 9 6.0 26 17.3 115 76.7 42-53 130 7 5.4 15 11.5 108 83.1 54-59 39 0 0.0 10 25.6 29 74.4 Total 630 40 6.3 97 15.4 493 78.3

Figure 4 below shows the graphical representation of distribution of HFA of surveyed children in relation to reference children (based on WHO standards). There is a slight drift to the left implying that the surveyed children were stunted in comparison to WHO standard curve with a mean± SD of -1.10±1.20.

Figure 4: Graphical representation of HFA distribution in comparison with WHO standards

3.4. Children Morbidity and Health Seeking According to the UNICEF conceptual framework on the causes of malnutrition, disease is categorized as one immediate cause alongside inadequate diet. There is a relationship between the

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two whereby disease may alter food intake while inadequate intake of some key nutrients may lead to infection. Ultimately they all lead to one outcome; malnutrition.

Assessment was done on the diseases that affected children 6 to 59 months in the past 2 weeks. Caregivers were asked whether their children had been ill in the past 2 weeks prior to the survey date. Those who gave an affirmative answer to this question were further probed on what illness affected their children and whether they sought assistance and where they sought any assistance when their child/children were ill. Those who indicated that their child/children suffered from watery diarrhea were probed on the kind of treatment that was given to them.

Among the children assessed 41.8% were ill in the past 2 weeks prior to the survey date. Among those who were sick, majority (41.5%) suffered from ARI/Cough, 37.9% fever with chills and 12.6% suffered from watery diarrhea. Table 14 below is a summary of morbidity status of children 6 to 59 surveyed. Table 14: Children Morbidity

% Prevalence

Diseases February 2019 Survey February 2018 survey

n Percent n Percent

All 277 41.8% 328 51.3%

Fever with chills 105 37.9% 149 68%

ARI/Cough 115 41.5% 175 80%

Watery diarrhea 35 12.6% 42 19%

Bloody diarrhea 0 0.0% 2 1%

Other infections 22 7.9% 0 0%

3.4.1. Therapeutic Zinc Supplementation during watery diarrhea episodes Based on compelling evidence from studies that, the efficacy of zinc supplementation reduces the duration and severity of diarrhea. In 2004 WHO and UNICEF recommended incorporating zinc supplementation (20 mg/day for 10-14 days for children 6 months and older, 10 mg/day for children under 6 months of age) as an adjunct treatment to low osmolality oral rehydration salts (ORS), and continuing child feeding for managing acute diarrhea. Kenya has adopted these recommendations (Innocent report 2009). According to Kenyan policy guideline on control and management of diarrheal diseases in children below five years in Kenya, all under-fives with diarrhea should be given zinc supplements as soon as possible. The recommended supplementation dosage is 20 milligrams per day for children older than 6 months or 10 mg per day in those below the age six months, for 10–14 days during episodes of diarrhea. This survey sought to establish the number of children who suffered from watery diarrhea and supplemented with zinc. All the children (100%)who suffered from watery diarrhea were supplemented with zinc.

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3.4.2. Health Seeking Majority of caregivers (61.7%) whose children fell ill in the past 2 weeks prior to the survey date sought assistance. Among those who sought assistance, 66.1% did so in public clinic while 25.7% did so in private clinic and 4.7% did so in a shop or kiosk. Overall 93.0% of caregivers whose children were sick sought assistance from appropriate sources such as public clinic, private clinic or mobile clinic as shown in figure 5 below.

Figure 5: Health Seeking Places

3.5. Child Immunization, Vitamin A and Micronutrients Supplementation and Deworming

3.5.1. Immunization Kenya aims to achieve 90% under one immunization coverage by the end of second medium term plan (2013- 2017). The Kenya guideline on immunization define a fully immunized child as one who has received all the prescribed antigens and at least one Vitamin A dose under the national immunization schedule before the first birthday.

This survey assessed the coverage of 4 vaccines namely, BCG, OPV1, OPV3, and measles at 9 and 18 months. From this assessment, 94.4% of children were confirmed to have been immunized by BCG based on the presence of a scar. Those who were immunized by OPV1 and OPV3 were 96.6% and 95.0% respectively while 91.8% and 71.9% had been immunized for measles at 9 and 18 month respectively, as indicated in figure 6 below.

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Figure 6: Immunization Coverage

3.5.2. Vitamin A Supplementation and Deworming Evidence shows that, giving vitamin A supplements to children reduces the rate of mortality and morbidity. Vitamin A reduces mortality risk by 24% (WHO 2011). Guaranteeing high supplementation coverage is critical, not only to eliminating vitamin A deficiency as a public-health problem, but also as a central element of the child survival agenda. Delivery of high-dose supplements remains the principal strategy for controlling vitamin A deficiency. Food-based approaches, such as food fortification and consumption of foods rich in vitamin A, are becoming increasingly feasible but have not yet ensured coverage levels similar to supplementation in most affected areas (UNICEF 2007). Poor data management on vitamin A logistics, inadequate social mobilization to improve vitamin uptake and placement of vitamin A at lower level of priority among other interventions has been cited as major challenges in achieving the supplementation targets (MOH Vitamin A Supplementation Operational Guidelines for Health Workers 2012). To assess vitamin A supplementation, parents and caregivers were probed on the number of times the child had received vitamin A in the past one year. Reference was made to the child health card and in case the card was not available recall, method was applied. Among those supplemented, 57.6% was confirmed by the use of health cards with 42.4% who were confirmed by recall. Analysis of vitamin A supplementation for children aged 6-11 months indicates that 66.7% of this age group had been supplemented with vitamin A. Among those aged 12 to 59 months, 56.1% had been supplemented with vitamin A twice in the past one year. Figure 7 illustrates the comparison of vitamin A supplementation between 2018 and 2019 surveys. Assessment on deworming for children aged 12 to 59 months indicates a small uptake of deworming drugs; only 42.8% had taken de-wormers twice in the past one year. Low Vitamin A supplementation and deworming was attributed to longer distances to the health facilities as children from villages far away from health facilities were more likely not to be supplemented with vitamin A or dewormed.

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Figure 7: Trends of Vitamin A supplementation

3.6. Maternal Nutrition Maternal nutrition has a direct impact on child survival. Pre- pregnancy nutrition influences the ability of a woman to conceive determines the fetal growth and development and the size of the fetus and its overall health and that of the mother. Maternal nutrition was assessed using maternal MUAC for all women of reproductive age and iron and folic acid supplementation for women with children under two years of age. WHO recommends daily consumption of 60mg elemental iron as well as 0.4mg folic acid throughout the pregnancy (WHO 2012). These recommendations have since been adopted by Kenya government in its 2013 policy guidelines on supplementation of FEFO during pregnancy. Overall 586 women of reproductive age participated in the survey. Almost half of them (45.0%) were lactating. Figure 8 below shows the physiological status of women who participated in the survey.

Figure 8: Physiological status of WRA

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The nutrition status of women was determined using MUAC. Women with MUAC less than 21 cm are classified as malnourished. Among the women of reproductive age, 3.1% (18) were malnourished. Of these malnourished women, 33.3% (6) were PLW. Table 15 below is a summary of maternal nutrition status.

Table 15: Maternal Nutrition Status

Indicator N (Total) n Percentage MUAC All women of reproductive age < 21 cm (malnourished) 586 18 3.1% >21cm 586 568 96.9% MUAC Pregnant and lactating women < 21 cm (malnourished-PLW) 346 6 1.7% >21 PLW 346 12 98.3%

50.9% of women with children below 2 years of age had been supplemented with iron and folic acid during their immediate pregnancy. The mean number of days IFAS was consumed by these women was 57.6 days. Table 16 below is a summary of iron and folic acid consumption in days.

Table 16: IFAS Consumption in days

IFAS Consumption in days n Percentage < 90 Days 164 64.8% 90≥180 Days 88 34.8% > 180 Days 1 0.4%

3.7. Water Sanitation and Hygiene Practices

3.7.1 Main Water Sources, Distance and Time to Water Sources Everyone has the right to water. This right is recognized in international legal instruments and provides for sufficient, safe, acceptable, physically accessible and affordable water for personal and domestic uses. An adequate amount of safe water is necessary to prevent deaths due to dehydration, to reduce the risk of water-related disease and to provide for consumption, cooking, and personal and domestic hygienic requirements. According to SPHERE handbook for minimum standards for WASH, the average water use for drinking, cooking and personal hygiene in any household should be at least 15 liters per person per day. The maximum distance from any household to the nearest water point should be 500 meters. It also gives the maximum queuing time at a water source which should be no more than 15 minutes and it should not take more than three minutes to fill a 20-litre container. Water sources and systems should be maintained such that appropriate quantities of water are available consistently or on a regular basis.

75% of the households in Tana River County obtained their water from improved water sources such as piped water system, protected boreholes, springs and shallow wells. The rest obtained their

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drinking water from unsafe sources such as unprotected shallow well (3.8%), river or spring (7.8%), earth pan/dam (5.4%), as well as water trucking (2.3%) as shown in figure 9 below.

Analysis of distance to water sources showed that most households (73.0%) obtain their water from sources less than 500 meters or less than 15 min. The rest obtained their water from sources between 500 meters to 2km (or 15 minutes to 1 hour) (24.5%) and more than 2km or 1 to 2hours to water sources (2.6%) as shown in table 17 below. With regard to queuing for water, 39.8% of household reported to queue for water. Among those who queue for water, 73.6% queue for less than 30 minutes, 14.7% between 30 and 60 minutes while 11.7% queued for more than 1 hour.

Figure 9: Main sources of drinking water

Table 17: Distances to water sources

Distances to water n(2019) Percent(2019) Percent(2018) sources Less than 500m (less 486 73.0% 69.6% than 15 min) 500m to 2km (15m to 163 24.5% 23.8% 1hr) More than 2km 17 2.6% 6.7%

3.7.2. Water Treatment Only 17% of the household surveyed treated their water. Among those who treated their water, 91.3% used chemicals such as water guard, PUR among others. Others boiled their water (3.5%) and use of traditional herbs (5.2%).

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Figure 10: Treatment of drinking water

3.7.3. Water Storage and Payment Although majority of households do not treat their water, majority of the households store their water in closed containers (95.5%) where it is likely to have physical contamination. The rest (4.5%), store it in open container where it is exposed to physical contamination. 35.4% of households consumed less than 15 liters of water prior to survey date. Among the household surveyed, 58.9% purchased their water. Among those purchasing, 41.8% purchased their water on monthly basis while the rest (58.2%) did it in terms of 20-liter jerry cans.

3.7.4. Hand washing The importance of hand washing after defecation and before eating and preparing food, to prevent the spread of disease, cannot be over-estimated. Users should have the means to wash their hands after defecation with soap or an alternative (such as ash), and should be encouraged to do so. There should be a constant source of water near the toilet for this purpose. (SPHERE Handbook 2004).

Majority of respondents (69.4%) were aware of hand washing practices. In term of practice and based on 24-hour recall, 35.9% of the respondents washed their hands before eating, while 31.4% did it before cooking. Among the caregivers, only 8.3% washed their hands after taking a child to toilet. Table 18 below is a summary of hand washing practices. Those washing their hands in all 4 critical moments were only 12.6%

Table 18: Hand washing practices

Hand washing Practice Households Percentage

HH aware of hygiene practices 462 69.4%

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After toilet 365 31.4%

Before cooking 228 19.6%

Before eating 418 35.9%

After taking a child to toilet 97 8.3%

Hand washing in 4 critical moments 58 12.6%

Hand washing with soap and water 306 45.9%

3.7.5. Sanitation Facilities Ownership and Accessibility If organic solid waste is not disposed of well, major risks are incurred due to fly breeding and surface water pollution, which is a major cause of diarrheal diseases. Solid waste often blocks drainage channels and leads to environmental health problems associated with stagnant and polluted surface water. Analysis of relieving points revealed that, most household are still relieving themselves in bushes and other open places. Open defecation was practiced by 57.1% of the households as indicated in figure 11 below.

Table 19: Household relieving point

Relieving point No. of HH Percentage Flush / pour flush 15 2.3% Pit latrine 265 39.8% Composting toilet 1 0.2% Hanging toilet / hanging latrine 2 0.3% No facility / bush / field 380 57.1% Other 3 0.5%

Figure 11: Relieving points

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3.8. Household and Women Dietary Diversity

3.8.1. Household Dietary Diversity (HDD) The household dietary diversity score (HDDS) is meant to reflect, in a snapshot form, the economic ability of a household to access a variety of foods. Studies have shown that an increase in dietary diversity is associated with socio-economic status and household food security (household energy availability) (FAO 2010). The HDDS is meant to provide an indication of household economic access to food, thus items that require household resources to obtain, such as condiments, sugar and sugary foods, and beverages, are included in the score. Individual dietary diversity scores aim to reflect nutrient adequacy. Studies in different age groups have shown that an increase in individual dietary diversity score is related to increased nutrient adequacy of the diet. Dietary diversity scores have been validated for several age/sex groups as proxy measures for macro and/ or micronutrient adequacy of the diet.

Household dietary diversity assessment was based on a 7 days’ recall period. At the data collection, 16 food groups as described in FAO 2010 guideline were used. The groups were combined at the analysis stage to come up with 12 food groups. As shown in figure 12 below, there was a high consumption of 4 food groups namely; Cereals (86.3%), milk and milk products (85.4%), Oils and Fats (85.4%), and sweets (85%) Few households (2.3%) consumed eggs.

Figure 12: Foods consumed at the household level based on 24 hours recall

The Minimum Dietary Diversity for WRA (MDD-W) indicator is a food group diversity indicator that has been shown to reflect one key dimension of diet quality: micronutrient adequacy. MDD- W is a dichotomous indicator of whether or not women 15–49 years of age have consumed at least five out of ten defined food groups the previous day or night. The proportion of women 15–49 years

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of age who reach this minimum in a population can be used as a proxy indicator for higher micronutrient adequacy, one important dimension of diet quality. As indicated in figure 13 below, the most consumed food was grains, white roots and tubers (96.2%) and dairy products (64.5%).

Figure 13: Women Dietary Diversity based on 24 hour recall

Further analysis shows that 6.5% consumed at least 5 food groups which is the Minimum dietary diversity for women as shown in table 20 below.

Table 20: Minimum Women Dietary Diversity

Number (Feb - (Feb. 2019 (Feb. 2018) 2019) WRA consuming 5 FGs or more 38 6.5% 18.1%

WRA consuming less than 5 food 548 93.5% 81.9%

Mean No. of food groups 2.9

3.9. Food Consumption Score (FCS) The Food Consumption Score is a composite score based on dietary diversity, food frequency and relative nutrition importance of different food group (WFP 2015). FCS is a proxy for household food security and is designed to reflect the quality of people’s diet. The FCS is considered as an outcome measure of household food security. Food consumption score classifies households in to 3

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categories namely, poor, borderline and acceptable. In computing FCS, 16 food groups were collapsed to 8 groups namely; cereals, pulses, vegetables, fruits, meats (meats, fish and eggs), dairies, sugars and oils. The frequency of consumption (maximum 7 days) was multiplied by an assigned weight factor i.e. cereals (2), pulses (3), vegetables (1), fruits (1), meats (4), dairies (4), oils (0.5) and sugar (0.5). Food consumption score (FCS) was obtained by summing up the product of each food item after which classification was done as illustrated in table 21 below.

Table 21: Food consumption score

Households N (%) Classification(Thresholds) Poor (0-21) 32 4.8% Borderline(21.5-35) 57 8.6% Acceptable (Above 35.5) 577 86.6%

Further analysis was done on diet quality based on vitamin A rich, iron rich and protein rich diets. As illustrated in figure 14 below, 1.10% of households which were classified under poor and borderline categories consume Vitamin A rich foods, while 4.5% consumed none of Iron rich foods, 3.4% consumed protein rich foods frequently. Among those households classified as acceptable, 73.1% consumed Vitamin A rich foods frequently, 91.3% consumed protein rich foods and only 34% consumed iron rich foods frequently.

Micronutrients Analysis Based on FCS 0.00% Acceptable 8.70% 91.30%

s_Cat Poor/Borderline 25.80% 70.80% 3.40% MN_Protein

Acceptable 29.50% 36.60% 34.00% t

Poor/Borderline 66.30% 29.20% 4.50% MN_iron_Ca

Acceptable 5.40%21.50% 73.10% at

Poor/Borderline 50.60% 48.30% 1.10% MN_VitA_C

0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00%

None Some (1 to 5 days) Frequent (6 to 7 days)

Figure 14: Micronutrient analysis based on FCS

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The Coping Strategies Index is a simple and easy-to-use indicator of household stress due to a lack of food or money to buy food. The CSI is based on a series of responses (strategies) to a single question: “What do you do when you don’t have adequate food, and don’t have the money to buy food?” The CSI combines, the frequency of each strategy (how many times was each strategy was adopted) and the severity (how serious is each strategy). This indicator assesses whether there has been a change in the consumption patterns of a given household. For each coping strategy, the frequency score (0 to 7) is multiplied by the universal severity weight. The weighted frequency scores are summed up into one final score (WFP 2012). 43.6% of household were food insecure in the past 7 days (they at one point lacked food or did not have money to buy food at one point. Table 22 below summarizes the coping strategies adopted by the households in such instances.

Table 22: Coping Strategies

No. of Frequenc Weighted Severity Coping Strategy Household y Score Score(Feb Score (1-3) s (0-7) 2019) Rely on less preferred or less 131 2.7 1 2.7 expensive foods Borrow foods from relatives or friends 133 2.6 2 5.2 Limit Portion sizes 148 2.9 1 2.9 Restrict consumption by adults so that 109 2.2 3 6.6 children can feed Reduce the number of meals 180 2.5 1 2.5 Total Weighted Coping Strategy Index 19.9

Comparison was also done from June 2014 SMART survey. The total weighted CSI from 2014 SMART survey was 8.3 (Jun2014) lowest and 26.8 (Jul 2016) highest. Figure 15 below illustrates the comparison of 2014 to 2019 assessment. There has been an increase in CSI meaning households are more food insecure in 2019 compared to Jan 2018.

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Figure 15: CSI Trend

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4.0. CONCLUSION AND RECOMMENDATIONS

4.1. Conclusion. There was no significant statistical difference between wasting for children under-five years between SMART survey February 2018 (GAM 15.6%) and February 2019 (GAM 14.8%). There was also no significant statistical difference between other childhood malnutrition indicators; underweight and stunting. The county is in phase 3 (Serious) according to IPC classification for acute malnutrition using GAM by WHZ.

Analysis was done on food security and morbidity issues which would have contributed to changes in acute malnutrition. Short rain assessment was done concurrently with the SMART survey. The food security situation in Tana River County was classified as “Stressed” (IPC Phase 2) in the mixed farming and marginal farming zones whereas the pastoral livelihood is classified as “Crisis” (IPC Phase 3).

In terms of morbidity, the proportion of children who were sick in the past 2 weeks reduced from 51.3 % in February 2018 to 35.2% in February 2019. There was a decrease in fever and chill (from 68.0% to 37.9%) and ARI/Cough (from 80%– 41.8 %) was noted.

Although there was no significant difference between 2018 and 2019 surveys, the stunting and underweight levels remained relatively high that requires medium and long term interventions by the county, National and partners to ensure they decrease. There was no significant difference in the two indicators between boys and girls.

71.7% of caregivers whose children were sick sought assistance from appropriate sources such as public clinic, private clinic or mobile clinic a good sign for health seeking behavior. All those who suffered from watery diarrhea were supplemented with zinc in the county. There was low Vitamin A & deworming coverage with only 48.0% had been supplemented with vitamin A twice in the past one year and 11.1% had taken de-wormers twice in the past one year.

Among women with children below 2 years of age, 86.3% had been supplemented with iron and folic acid during their immediate pregnancy. The mean iron and folic acid consumption was 48.1 days. None of the surveyed women had consumed iron and folic acid in the recommended 270 days Only 17.0% of the household surveyed treated their water and majority of respondents (69.4%) were aware of hand washing practices however, those who practiced hand washing in 4 critical moments were only 12.6%.

Open defecation was practiced by 57.1% of the households while toilet ownership remained low at 42.9%

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4.2. Recommendations Based on the above findings, the following interventions are recommended.

Findings Recommendations Actors (By Timelines Who)M GAM rate of  Active case findings at the MOH/CONCE APRIL 2019 14.8% community and Nutrition RNWORLDWI surveillance. DE/KRCS June 2019  Scale up of IMAM surge activities at health facilities implementing IMAM.  Continued scale up of MIYCN activities (BFCI and BFHI) as well as IMAM activities  Conduct rapid assessment in the identified malnutrition hotspots  Conduct integrated medical outreaches  Establish a Multi-sectoral platform for high level advocacy and coordination of nutrition activities both sensitive and specific and advocate for recruitment of more nutritionist to help boost nutrition service delivery. 57.1% of the  Sensitize communities on WASH MOH/CONCE September 2019 population  Sensitize schools on WASH through RNWORLDWI practice OD school health clubs DE/KRCS  Scale up CLTS activities in all the CUs within the county 12.6%  Sensitize the community on MOH,CONCER JUNE 2019 practice importance of handwashing in 4 NWORLDWID handwashing critical times E/KRCS in 4 critical  Use local FM radio to sensitize the times community on handwashing 0.4% of  Sensitize the communities of MOH,CONCER JUNE 2019 pregnant importance of consuming IFAS NWORLDWID women during pregnancy through media. E/KRCS consume  Train health care providers on IFAS IFAS for guidelines >180 days Vitamin A  Sensitize the community on the MOH,CONCER May & November supplementa importance of Vitamin A NWORLDWID 2019 tion 12 to 59 supplementation and deworming as E/KRCS months (2 well as scale up VAS interventions doses)- within ECDE, Duks and at the 51.6% community

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REFERENCES 1. WHO Guideline: Updates on the management of severe acute malnutrition in infants and children 2013

2. Klemm RDW, Harvey PWJ Wainwright E, Faillace S, Wasanwisut, E. Micronutrient programs: what works and what need more work? A report of 2008. Innocent process, August 2009, micronutrient forum, Washington DC.

3. WHO e- library of evidence of nutrition action (E LENA); zinc supplementation in management of diarrhea

4. Kenya policy guidelines on control and management of diarrhea diseases in children below 5 years in Kenya (March 2010)

5. Kenya national guidelines on immunization (2013)

6. WHO guideline: Vitamin A supplementation in infant and children 6 to 59 months of age, Geneva, World Health Organization 2011.

7. UNICEF (2007), Vitamin A supplementation; A decade of progress.

8. Ministry of Health (2013), maternal, infant and young child nutrition; National operation guideline for health workers

9. The SPHERE project (2004), Humanitarian charter and minimum standards in disaster response

10. FAO (2010); guidelines for measuring household and individual dietary diversity.

11. WFP (2015), Food Consumption Score, Nutrition Quality Analysis (FCS-N)

12. FAO and FHI 360, 2016. Minimum Dietary Diversity for Women: A guideline for measurement. Rome: FAO

13. KFSSG (2018), Long Rain Assessment, Tana River County.

14. Tana River County SMART Survey (February 2018)

APPENDICIES

APPENDIX 1: Overall Score of the Survey

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Standard/Reference used for z-score calculation: WHO standards 2006 (If it is not mentioned, flagged data is included in the evaluation. Some parts of this plausibility report are more for advanced users and can be skipped for a standard evaluation) Indicator Acceptable Survey Area values/range

Flagged data (% of out of range subjects) <7.5 0 (2.4 %)

Overall sex ratio (significant CHI square) >0.001 0 (p=0.161)

Age ratio (6-29vs 30-59) Significant CHI square >0.001 2 (p=0.075) Dig. prevalence score-weight <20 0 (4)

Dig. prevalence score-height <20 0 (5)

Dig. prevalence score-MUAC <20 0 (4) Standard Dev. Height WHZ >0.80 0 (1.04) Skewness WHZ <±0.6 0 (-0.09) Kurtosis WHZ <±0.6 0 (-0.10)

Poisson WHZ -2 >0.001 3 (p=0.005)

OVERALL <24 5 % (Excellent)

Appendix 2: Sampled Clusters SUB COUNTY WARD VILLAGES CLUSTER NUMBER BURA Bangali TULA 29

BURA Bangali KURITI 30

BURA Bangali BOKA/TARA RC

BURA Bangali EL-BOMBI A 31

BURA Chewele DUDU MADEO/BILBIL SCHOOL 40

BURA Chewele KHOTIOLOW 41

BURA Chewele BAWAMA 42

BURA Hirimani VI SHIRIKISHO BULA WACHU /V 2 37

BURA Hirimani TUMAINI NORTH/MANYATA 38

BURA Hirimani BULA MARARA/TYPE CDEF 39

BURA Madogo MOYE BUYA 32

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BURA Madogo BULA KARATASI 33

BURA Madogo BARA /KONE BURKITI 36

BURA Sala CARLFONIA A 34

BURA Sala BAKUYU A 35

GALOLE Chewani CHEWANI A 20

GALOLE Chewani AMANI 21

GALOLE Chewani GHALAMANI 23

GALOLE Chewani MWANGAZA A 25

GALOLE Kinakomba Maroni RC

GALOLE Kinakomba FANJUA 27

GALOLE Mikinduni KONE B 22

GALOLE Mikinduni HANDAMPIA MISSION 28

GALOLE Wayu TESO RC

GALOLE Wayu KALALANI 24

GALOLE Wayu Golecha 26

TANA DELTA Garsen Central BANDI 4

TANA DELTA Garsen Central Kipao A 14

TANA DELTA Garsen Central BURAKRASH 15

TANA DELTA Garsen North BAHATI RC

TANA DELTA Garsen North MNAZINI RC

TANA DELTA Garsen North KITERE 1

TANA DELTA Garsen North HAMESA C 2

TANA DELTA Garsen North CHIRA A 3

TANA DELTA Garsen North ABAGANDA 5

TANA DELTA Garsen South IDSOWE 6

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TANA DELTA Garsen South UMOJA 7

TANA DELTA Garsen South ODA ORMA 8

TANA DELTA Garsen west Assa 9

TANA DELTA Kipini East KALOLENI A 16

TANA DELTA Kipini East KIBAONI A 17

TANA DELTA Kipini East TAZAMALAKO 18

TANA DELTA Kipini East ZAMZAM A 19

TANA DELTA Kipini West ON WARDEI A 10

TANA DELTA Kipini West BAHATI (MNAZINI) 11

TANA DELTA Kipini West CHAMWANAMUMA 12

TANA DELTA Kipini West SHIRIKISHO 13

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Appendix 3: Calender of Events TANA RIVER COUNTY CALENDER OF EVENTS, February 2019 MON SEASO 2014 2015 2016 2017 2018 TH NS 2019 JANU New ARY year/school New opening/An New year/school New imal year/school opening/Anim year/school Migration/ opening/Anim al Migration/ opening/Anim Nyongoro al Migration/ Elade Attack/ al Migration attack/ mangoes mangoes mangoes mangoes season/ 49 season 37 season/ 25 season/ 13 1 Cholera Outbreak Delta/ Voter FEBR 9TH FEB registration/ UAR ONWARD Drought(Bona/ Drought Y Dry S Ukame/Jilal)/ (Bona/Ukame/J Long rains/ season( Mangoes Maulid/mango mangoes ilal) /mangoes mangoes Bona) season es season/ 48 season/ 36 season/ 24 season/ 12 0 Death of Deputy MAR Speaker Drought Flooding CH Tana River Easter (Bona/Ukame/J /Handshake / 59 47 Holiday/ 35 ilal) / 23 / 11 Cholera outbreak in Bura/Hola- Garissa Road APRI carried L away by Long Garissa floods/ rains Easter University Easter (Furmat Holidays/S attack/ School Easter Holidays/Sc ha/chii chool holidays/ Lucy Kibaki's Holidays/Scho hools mo/Gan Holidays/ Easter Death/School ol Holidays/ holidays/ 10 n) 58 Holidays/ 46 holidays./ 34 22 MAY World cup/ World cup/ Waldena Ramadhan/ Hyena Labour day/ Labour Day

Attack/ope Opening of Opening of Ramadhan/Op /Opening ning of schools/ schools/Labour ening of schools schools/ Labour day/ 45 day/ 33 schools/ 21 9

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Labour day/ 57 JUNE Mpeketoni Nurses Attack/ Strike/Cholera Ramadhan Ramadhan/ Ramadhan/ Outbreak Madaraka

Cold / Madaraka Madaraka Day/ Madaraka Day/ Delta/Eid -Fitr/ Day/Eid - Season Day/56 44 32 20 fitr/ 8 JULY (Damoc Obama h/sika) Kenya's Visit/ Mosquito nets Eid-Fitr/ Nurses Polio

Eid-Fitr/ distribution/Ei Burning of Strike/Eid Campaign/ 55 d-Fitr 43 schools/ 31 Hajj/ 19 7 Eid-Hajj/ AUG Schools UST Beyond Zero / Nurses Holidays/Po School Strike/Election lio School School Holidays/Eid- s/Schools Campaign/ Dry Holidays/ Holidays/ Hajj/ holidays/ Harvesting Season( Harvesting Harvesting Harvesting Harvesting season/ Odoles) season/ 54 season/ 42 season/ 30 season/ 18 6 SEPT EMB Opening of Nurses Strike/ ER schools/Eid Opening of Opening of Opening of -Hajj/ 53 schools/ 41 schools/ 29 schools/ 17 5 OCT Nurses OBER Mashujaa Strike/Mashuja Mashujaa Day/KCPE Mashujaa a Day/KCPE Day/KCPE Exams/Eid- Day/KCPE Exams/Repeat Exams/ 52 Hajj/ 40 Exams/ 28 Elections/ 16 4 NOV Mandera EMB Teachers ER attack/ KCPE KCSE Exams/ Nurses Exams/ Pope Visit to KCSE Strike/KCSE Short Arrival of Kenya/ Exams/Arrival Exams/ rains(H fishermen Arrival of of fishermen Arrival of agey) from fishermen from Pemba to fishermen Pemba to from Pemba to Delta from Pemba to Delta/ 51 Delta/ 39 27 Delta/ 15 3 DECE Christmas MBE Holidays/Ja Christmas Christmas Christmas R mhuri Day Holidays/Jamh Holidays/Jamh Holidays/Jamh / School uri Day / uri Day / uri Day/ long School long School long School Long holidays/ holidays/Marri holidays/Marri Holidays/ Marriage age age Marriage ceremonies Ceremonies/ Ceremonies/M Ceremonies/M 2

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/ Mangoes Mangoes angoeseason/ angoes season/ season/ 50 season/38 26 14

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Appendix 4: SMART Questionnaire

1.IDENTIFICATION 1.1 Data Collector______1.2 Team Leader______1.3 Survey date (dd/mm/yy)------1.4 County 1.5 Sub 1.6 Ward 1.7 1.8 Sub- 1.9 1.10 Cluster 1.11 HH 1.12 County Location Location Village No No Team No.

1.13 Latitude Longitude Household ______geographical _ __ coordinates

2. Household Demographics 2.1 2.2a 2.2b 2.3 2.4 2.5a 2.6 2.7a 2.7b 2.8 2.10a go to 2.5b, c and d before proce eding to 2.6 Age Please Please Age Childs Sex If Main 2.7a, Wh If the Gro give me indicate (Record age betwee reason What is at is househo up the the age in verifie 1= n 3 and for not the the ld owns names of househol MONTHS d by Male 18 years attendin child hig mosquit the d head for old, Is g school doing hest o net/s, persons (write children 1=Heal 2= the (Enter when leve who who HH on <5yrs and th card Femal child one not in l of slept usually the YEARS 2=Birt e attendin code school? edu under live in member’ for those h g from cati the your s ≥ certific school? list) 1=Work on mosquit househol column) 5 years’s) ate/ 1=Chro ing on attai o net d. Yea M notific nic family ned last rs ont ation Sicknes farm ?(le night? hs 3=Bapt 1 = Yes s 2=Herdi vel (Probe- ism 2 = No 2=Weat ng com enter all card (If yes her Livestoc plet respons 4=Reca go to (rain, k ed) es ll 2.8; If floods, 3=Wor Fro mentio 5. no go t storms) king for m 5 ned other o 2.7) 3=Famil paymen yrs (Use 1 if ______y labour t away and “Yes” 2 __ responsi from abo if “No specify bilities home ve and 3 if not

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4=Wor 4=Left 1 applica king home =Pr ble) go outside for e to home elsewhe pri questio 5=Teac re mar n 2.11 her 5=Child y absente living 2= eism/lac on the Pri k of street mar teachers 6: y 6= Fees Other 3=S or costs specify eco 7=Hous ______nda ehold ____ ry doesn’t 4=T see erti value of ary schooli 5= ng Non 8 =No e food in 6=o the ther schools s(sp 9 = ecif Migrate y) d/ Go moved to from que school stio area n to (includi 2.9 ng ↓ displace ments) 10=Inse curity/v iolence 11-No school Near by 12=Mar ried 13. Pregna nt/ taking care of

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her own child 14. attendi ng Duksi/ Madras a 15. too young for school 13=othe rs (specify )……… ……… ….. < 5 1 YR 2 S 3 4 >5 5 TO 6 <18 7 YR 8 S 9 10 11 12 AD 13 UL 14) T 15 (18 16 yea rs and abo ve) 2.5c. 2.5d 2.5e Total Total Total number of number number children below of ALL of 2 years (0-23 people in children months) the under 5 years (0- ______Househo

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ld 59 includin months) g children ______------

2.9 How many mosquito nets does this household have? ______(Indicate no.) go to question 2.10a before proceeding to question 2.10b 2.11 Main Occupation of the Household Head – HH. 2.12. What is the main current (enter code from list) source of income of the household? 1=Livestock herding 1. =No income 2=Crop farming/Own farm labour 2. = Sale of livestock 3=Employed (salaried) 3. = Sale of livestock products 4=Waged labour (Casual) 4. = Sale of crops 5=Petty trade 5. = Petty trading e.g. sale of 6=Merchant/trader firewood 7=Firewood/charcoal 6. =Casual labor 8=Fishing 7. =Permanent job 9= Income earned by children 8. = Sale of personal assets 9. = Remittance 10=Others (Specify) |____| 10. Other-Specify |____| 2.13 Marital status of the respondent 2.14. What is the residency status of 1. = Married the household? 2. = Single 1. IDP 3. = Widowed 2.Refugee 4. = separated 3. Resident 5. = Divorced. |____| |____| 2.15 Are there children who have come to live with you 2.15b If yes, why did the recently? child/children come to live with you? 1. YES 2. NO 1= Did not have access to food 2=Father and Mother left home 3=Child was living on the street, 4=Care giver died 5= Other specify ______

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Fever with Cough/ARI: Any Watery diarrhoea: Bloody diarrhoea: Any Malaria: episode with Any episode of three episode of three or High severe, persistent or more watery stools more stools with blood temperature cough or difficulty per day per day with shivering breathing

3. 4. 5. 6. 7. 8. CHILD HEALTH AND NUTRITION (ONLY FOR CHILDREN 6-59 MONTHS OF AGE; IF N/A SKIP TO SECTION 3.6) Instructions: The caregiver of the child should be the main respondent for this section 3.1 CHILD ANTHROPOMETRY 3.2 and 3.3 CHILD MORBIDITY (Please fill in ALL REQUIRED details below. Maintain the same child number as part 2) A B C D E F G H I J K L M N 3 3. 3. 3. 3.3 c C . 2 3 3 h 2 b a b i a l d N o . wha SE E Ag W He O M W H C I If H If W If If the child t is X x e eig ig e U a o h s y a Y h th had watery the Fe a in ht ht d A s w il t e s E e e diarrhoea in relat mal c mo (K (C e C c m d h s y S, n re the last ions e… t nt G) M) m ( h u ’s e t o w t sp TWO (2) hip ...F B hs XX X a c il c w c o u h h o WEEKS, of i .X X. Y m d h e h q r ic e ns did the the Mal r X = ) w di i il u c h c e child get: resp e t Y X ei d g d e h il h is 1. O ond ….. h e X g th h i st i l il y R ent …. D s . h e t n i l n d es S with M a N X e c v a o d e w to 2. Z the t = d hi e n n ( ss as q i chil e N at ld r y J. N ( si u n d/ch o b w if n w A m c es c ildre ir ei i u h M u k ti s n t g e t i E lt di o u 1=M h h d ri c ) i d n p othe ? ? b ti h b p y # p r … y o n e l o 3. l 2=F … : n u e e u 2 e

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athe 1. Y… 1 p t n r se w m r e… = r ri i e e h e 3=Si s… H o ti l s k er n blin 2. N… e g o l p as e t g o… a r n i o si di a 4=G 3. D… lt a p n n st d t rand o h m r t s a y i mot n c 1. Yo h e n o o her ’ a eg e s c u n 5=O t r sr p p e se ? ther k d 2. Na a o ? e Show (spe n 2 om s ss 1. k sample and cify) o = ? t i Y as probe w R If 1 t b es si further for If e n . w l 2. st this n c o O o e N a component o a s T w ) o n check the o ll k P e 1 ce remaining r i 2 e = ? drugs(confi d p . k F ( rm from o t S s e M mother n o F ? v or child ’t q P e e booklet) k u 3 1 r th n e . . w a o s B Y it n w ti S e h o s o F s c n k n P 2 h e i s O . il re p 3 t N ls sp t . h o li o o 2 e k ns M r I e e S f m p p N al os e o a si c , ri bl if s a e- y k 2 1. _ i = T _ p A ra _ t R di _ o I ti _ 3 / o _ . C n 4 o al u h

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g ea h le 3 r = 2. W C a o t m e m r u y ni d ty ia h r ea r lt h h o w e or a k 4 er = 3. B P l ri o va o te d cl y in d ic ia / r p r h h ar o m e ac a y 5 4. = S O h t o h p/ e ki r os (s k p 5. e P ci u f bl y ic ) cl

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S in e ic e 6. c M a o s bi e le d cl e in fi ic n 7. it R i el o at n iv s e a or b fr o ie v n e d 8. L oc al h er bs 9. N G O /F B O 0 1 1 , 2 , 3 0 2 0 3 0 4

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3.4 Maintain the same child number as part 2 and 3.1 above

A1 A2 B C D E F G H I Ch How Has How If FOR Has the Has Has Has Has ild many the man Vita CHIL child child child child child No times child y min DREN receive receiv receive receiv receiv . has receive time A 12-59 d BCG ed d OPV3 ed ed the child d s did recei MON vaccina OPV1 vaccina measle second recei vitami the ved THS tion? vaccin tion? s measle ved n A child how Check ation vaccin s Vita supple recei man How for 1=Yes, ation vaccin min ment ve y many BCG 1=Yes, Card at 9 ation A in the vita time times scar. Card 2=Yes, month (18 to in past 6 min s in has 2=Yes, Recall s 59 the month A the child 1 = scar Recall 3 = No (On month past s? caps past receiv 2=No 3 = No 4 = Do the s ) year? ules one ed scar 4 = Do not upper (On (sho from year drugs not know right the w the did for know should upper samp facili the worm er)? right le) ty or child s should out recei in the 1=Yes, er)? () reac ve past Card h in verif year? 2=Yes, 1=Yes, the ied (show Recall Card past by Sampl 3 = No 2=Yes, year Card e) 4 = Do Recall ? not 3 = No know 4 = Do not know 01

02

03

04

3.5 MNP Programme Coverage. Maintain the same child number as part 2 and 3.1 above. Ask all the relevant questions (3.5.1 to 3.6.4) before moving on to fill responses for the next child. THIS SECTION SHOULD ONLY BE ADMINISTERED IF MNP PROGRAM IS BEING IMPLEMENTED OR HAS BEEN IMPLEMENTED

3.5 Enrolment in an MNP program 3.6 Consumption of MNPs

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3.5.1.a Is MNP program available (program running in the past six month) in the survey area? Yes =1 No = 2 If ‘No’ skip section 3.5 and 3.6 and go to 3.7 3.5.1. b 3.5.2 3.6.1 3.6.2 3.6.3 3.6.4 Is the child If the child, 6- Has the child If yes, If no, What are the enrolled in the 23months, is not consumed how since reasons to MNP enrolled for MNP, MNPs in the frequent when did stop feeding program?(show give reason. last 7 do you you stop your child the example of (Multiple answers days?(shows give MNP feeding with MNPs? the MNP possible. Record the the MNP to your MNPs to (Multiple sachet) code/codes in the sachet) child? your answers (record the code respective child’s (record the (record child? possible. in the respective number. DO NOT code in the the code (record Record the child’s number) READ the answers) respective in the the code code/codes in child’s respective in the the respective Yes =1 Do not know about number) child’s respective child’s No=0 MNPs number) child’s number. DO …...... ………1 YES = 1 number) NOT READ If no go to 3.5.2, Discouraged from N0= 0 Every day the answers) If yes go to what I heard from ……...... 1 week to section 3.6.1 others If no skip to .……….1 2 weeks Finished all ……...... 3.6.3 Every ago ....1 of the sachets ...... 2 other day 2 week to ...... 1 The child has not ...... ….… 1 month Child did not fallen ill, so have not …..2 ago ....2 like it gone to the health Every More ...... facility …. third day than 1 2 ….....…..3 ……...... month Husband did Health facility or ……..3 ...... 3 not agree to outreach is far 2 days per give to the ….....…4 week at child Ch ild receiving any day ...... 3 therapeutic or ....4 Sachet got supplementary foods Any day damaged ...... 5 when I ………….4 Other reason, remembe Child had specify r..…5 diarrhea after ...…….....……….6 being given vitamin and Skip to 3.7 mineral powder ……..5 Child fell sick...... 6

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Forgot …………… ……….…..7 Child enrolled in IMAM program …8 Other (Specify)______..9

Child 1

Child 2

Child 4

MATERNAL NUTRITION FOR WOMEN OF REPRODUCTIVE AGE (15-49 YEARS)(Please insert appropriate number in the box) 3.7 3.8 3.9 3.10 3.11 Woman ID. What is the mother’s / Mother/ During the pregnancy If Yes, for how many (all women in the caretaker’s caretaker’s of the (name of the days did you take? HH aged 15-49 physiological status MUAC reading: youngest biological years from the 1. Pregnant ____.__cm child below 24 ( probe and household 2. Lactating months) did you take approximate the demographics – 3. not pregnant and the following number of days) section 2 ) not lactating supplements? indicate 4. Pregnant and 1. Yes lactating 2. No 3. Don’t know 4. N/A

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Iron Folic Combin Iron Fol Combi tabl acid ed iron table ic ned ets and ts aci iron syru folic syru d and p acid p folic supple acid ments supple ments

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4.1 WATER, SANITATION AND HYGIENE (WASH)/- Please ask the respondent and indicate the appropriate number in the space provided 4.1 What is the MAIN source of drinking water for 4.2 a What is the trekking distance to the 4.2b – the household NOW? current main water source? Who piped water 1=less than 500m (Less than 15 minutes) MAINLY piped into dwelling 11 2=more than 500m to less than 2km (15 to 1 goes to piped to yard / plot 12 hour) fetch piped to neighbour 13 3=more than 2 km (1 – 2 hrs) water at public tap / standpipe 14 4=Other(specify) your |____| current tube well / borehole 21 main water dug well source? protected well 31 unprotected well 32 1=Wome spring n, 2=Men, protected spring 41 3=Girls, unprotected spring 42 4=Boys

rainwater 51 tanker-truck 61 cart with small tank 71 water kiosk 72 surface water (river, dam, lake, pond, stream, canal, irrigation channel) 81

packaged water bottled water 91 sachet water 92

1. 4.2.2 How long do you queue for water? .3 Do you do anything to your water before a 1. Less than 30 minutes drinking? (MULTIPLE RESPONSES POSSIBLE) |____| 2. 30-60 minutes (Use 1 if YES and 2 if NO). 3. More than 1 hour 1. Nothing 4. Don’t que for water 2. Boiling………… 1. ……………………………………. |____| 3. Chemicals (Chlorine,Pur,Waterguard)…………… |____| 4. Traditional herb…………………………………….. . |____|

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5. Pot filters…………………………………… ……….. |____|

5.

4.3a 6.

|____| 4.4 Where do you store water for drinking? 4.5 How much water did your household use 1. Open container / Jerrican YESTERDAY (excluding for animals)? 2. Closed container / Jerrican (Ask the question in the number of 20 liter Jerrican and |____| convert to liters & write down the total quantity used in |____| liters)

4.6 Do you pay for water? 4.6.1 If yes, how much per 20 4.6.2 If paid per 1. Yes liters jerrican ______month how much 2. No (If No skip to Question KSh/20ltrs |____| 4.7.1) |____| 4.7.1 We would like to learn about where members 4.7.1b Is soap or detergent or ash/mud/sand a of this household wash their hands. present at the place for handwashing? Can you please show me where members of your household most often wash their hands? YES, PRESENT 1 Record result and observation. NO, NOT PRESENT ……………………2

OBSERVED FIXED FACILITY OBSERVED (SINK / TAP) IN DWELLING 1 IN YARD /PLOT 2 MOBILE OBJECT OBSERVED (BUCKET / JUG / KETTLE) 3

NOT OBSERVED NO HANDWASHING PLACE IN DWELLING / YARD / PLOT 4 NO PERMISSION TO SEE 5

4.7.1 Yesterday (within last 24 hours) at what instances did you wash your hands? (MULTIPLE RESPONSE- (Use 1 if “Yes” and 2 if “No”) 1. After |____| toilet…………………………………………………………………………………………… |____| ………………… |____| |____| |____|

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2. Before cooking………………………………………………………………………………………… ……………... 3. Before eating…………………………………………………………………………………………… ……………. 4. After taking children to the toilet……………………………………………………………………………………. 5. Others………………………………………………………………………………………… ……………………….

4.7.2 If the caregiver washes her hands, then probe 4.8 What kind of toilet facility do members of further; what did you use to wash your hands? your household usually use? 1. Only water 2. Soap and water If ‘Flush’ or ‘Pour flush’, probe: 3. Soap when I can afford it Where does it flush to? 4. traditional herb |____| 5. Any other specify |____| If not possible to determine, ask permission to observe the facility.

flush / pour flush flush to piped sewer system 11 flush to septic tank 12 flush to pit latrine 13 flush to open drain 14 flush to DK where 18 pit latrine ventilated improved pit latrine 21 pit latrine with slab 22 pit latrine without slab / open pit 23

composting toilet 31

bucket 41 hanging toilet / hanging latrine 51

no facility / bush / field 95

1. OTHER (specify) 96

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5.0: Food frequency and Household Dietary Diversity

*Type of Did If yes, mark days the food was What WOMEN DIETARY food* member consumed in the last 7 days? was the DIVERSITY s of your main ONLY FOR WOMEN househ 0-No source of AGE 15 TO 49 YEARS. old 1-Yes the REFER TO THE consum dominant HOUSEHOLD e any food item DEMOGRAPHICS food consume SECTION Q2.3 AND from d in the Q2.5 these HHD? Please describe the foods food 1.Own that you ate or drank groups productio yesterday during day and in the n night at home or outside last 7 2.Purchas the home (start with the days?(fo e first food or drink of the od must 3.Gifts morning) have from 0-No been friends/fa 1-Yes cooked/ milies D D D D D D D TO Wo Wo Wo Wo served 4.Food 1 2 3 4 5 6 7 TA man man man man at the aid L ID ID ID ID househ 5.Traded … … … … old) or … ….. …. ….. Bartered … 0-No 6.Borrow 1-Yes ed 7.Gatheri ng/wild fruits 8.Other (specify) Cereals and cereal products (e.g. sorghum, maize, spaghetti, pasta, anjera, bread)?

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Vitamin A rich vegetables and tubers: Pumpkins, carrots, orange sweet potatoes White tubers and roots: White potatoes, white yams, cassava, or foods made from roots Dark green leafy vegetables: Dark green leafy vegetables, including wild ones + locally available vitamin A rich leaves such as cassava leaves etc. Other vegetables (e.g., tomatoes, egg plant, onions)? Vitamin A rich fruits: + other locally available vitamin A rich fruits

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Other fruits Organ meat (iron rich): Liver, kidney, heart or other organ meats or blood based foods Flesh meats and offals: Meat, poultry, offal (e.g. goat/camel meat, beef; chicken/po ultry)? Eggs? Fish: Fresh or dries fish or shellfish Pulses/legu mes,(e.g. beans, lentils, green grams, cowpeas)? nuts and seeds Milk and milk products (e.g. goat/camel/ fermented milk, milk powder)? Oils/fats (e.g. cooking fat or oil, butter,

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ghee, margarine) ? Sweets: Sugar, honey, sweetened soda or sugary foods such as chocolates, sweets or candies Condiment s, spices and beverages:

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6. COPING STRATEGIES INDEX Frequency score: Number of days out of the past seven (0 -7). In the past 7 DAYS, have there been times when you did not have enough food or money to buy food? If No; END THE INTERVIEW AND THANK THE RESPONDENT If YES, how often has your household had to: (INDICATE THE SCORE IN THE SPACE PROVIDED) 1 Rely on less preferred and less expensive foods? 2 Borrow food, or rely on help from a friend or relative? 3 Limit portion size at mealtimes? 4 Restrict consumption by adults in order for small children to eat? 5 Reduce number of meals eaten in a day? TOTAL HOUSEHOLD SCORE: END THE INTERVIEW AND THANK THE RESPONDENT

HOUSEHOLD HUNGER SCALE

Question Response option 1 In the past [4 weeks/30 days], was there ever no food to eat 0 = No (Skip to Q2) of any kind in your house because of lack of resources to 1 = Yes get food? 1A How often did this happen in the past [4 weeks/30 days]? 1 = Rarely (1–2 times) 2 = Sometimes (3–10 times) 3 = Often (more than 10 times) 2 In the past [4 weeks/30 days], did you or any household 0 = No (Skip to Q3) member go to sleep at night hungry because there was not 1 = Yes enough food? 2A How often did this happen in the past [4 weeks/30 days]? 1 = Rarely (1–2 times) 2 = Sometimes (3–10 times) 3 = Often (more than 10 times) 3 In the past [4 weeks/30 days], did you or any household 0 = No (Skip to the next section) member go a whole day and night without eating 1 = Yes anything at all because there was not enough food? 3A How often did this happen in the past [4 weeks/30 days]? 1 = Rarely (1–2 times) 2 = Sometimes (3–10 times) 3 = Often (more than 10 times)

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4.2 FOOD FORTIFICATION (FF)/- Please ask the respondent and indicate the appropriate number in the space provided 1.1 Have you heard about food fortification? 1. Yes 2. No 3. Don’t know If yes, where did you hear or learn about it? (MULTIPLE RESPONSE ARE POSSIBLE- (Use 1 if “Yes” and 2 if “No”) 6. Radio…………………………………………………………………………………………… |____| ………………… |____| 1.1.1 7. Road |____| show…………………………………………………………………………………………… |____| …………... |____| 8. In a training session attended……………………………………………………………………………………. 9. On a TV show……………………………………………………………………………………. 10. Others………………………………………………………………………………………… ……………………….

1.2 Respondent’s knowledge on the food fortification logo (Show the food fortification logo to the respondent and record the response). Do you know about this sign? 1. Yes 2. No 3. Don’t know |____|

1.3 What is the MAIN source of Maize flour for the 1.1b Do you know if the maize flour you household NOW? consume is fortified or not? 2. Bought from the shops, supermarket e.t.c 3. Maize is taken for milling at a nearby Posho Mill 1. Yes 4. Bought from a nearby Posho Mill 2. No 5. Other (Please specify) 3. Don’t know |______|

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1.4 What brands of the following foods does your household consume? 1. Maize flour |______2. Wheat flour | 3. Margarine |______4. Oils | 5. Fats |______6. Sugar | |______| |______| |______|

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Appendix 5: Survey teams Team # Survey Team Members Team leader Name 1 Phedis Sanita Prisillah chepkemei Mohamed Safari 2 Jamila Dama Kimura Mbaabu Johora Ali 3 Peninnah Makena Emily Jarah Aden Dubow 4 AbdirizaQ Ismail Doris Adhiambo Sharifa Abdi 5 Ahmed Ibrahim Joyce Kombe Zaituni Bilal 6 Emily Malika Pauline Kamotho Abdi Guyo 7 Mteti Harry Racheal Rhaya Racheal Bada

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Appendix 6: Survey coodination

Coordination Team

Tana River County Department Makopa Omari (CNC Tana River County and overall survey of Health Coordinator) Flora Abio (Galole Sub County nutrition coordinator) Nicholas Musembi (UNICEF, Nutrition Support Officer), Catherine Mwangi(Concern Worldwide, M&E Officer), Shadrack Njoka Partner Supervisors (Concern Worldwide, Health and Nutrition Manager) Florence Njambi (Kenya Red Cross, Regional Nutrition Coordinator) Technical Support Kibet Chirchir (NITWG)

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